Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.7 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_itemsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 4 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
qtde_returns is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 25.16116391) Skewed
frequency is highly skewed (γ1 = 24.88077608) Skewed
qtde_returns is highly skewed (γ1 = 21.97906809) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2021-12-25 22:20:09.082438
Analysis finished2021-12-25 22:20:54.521485
Duration45.44 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.239475
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:54.753176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.914732
Coefficient of variation (CV)0.6710203022
Kurtosis-1.010612562
Mean2317.239475
Median Absolute Deviation (MAD)1271
Skewness0.3423730333
Sum6879884
Variance2417759.825
MonotonicityStrictly increasing
2021-12-25T18:20:55.041852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.32233
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:55.331736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15220
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.857469
Coefficient of variation (CV)0.1125619638
Kurtosis-1.205579452
Mean15270.32233
Median Absolute Deviation (MAD)1489
Skewness0.03229421811
Sum45337587
Variance2954470.998
MonotonicityNot monotonic
2021-12-25T18:20:55.636247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2692.968309
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:55.942249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32306.52
95-th percentile7166.028
Maximum279138.02
Range279131.82
Interquartile range (IQR)1735.56

Descriptive statistics

Standard deviation10133.7968
Coefficient of variation (CV)3.763058318
Kurtosis397.4333889
Mean2692.968309
Median Absolute Deviation (MAD)671.72
Skewness17.63828759
Sum7995422.91
Variance102693837.5
MonotonicityNot monotonic
2021-12-25T18:20:56.223222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
889.932
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2944)2949
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.33512967
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:56.514307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76056005
Coefficient of variation (CV)1.208679619
Kurtosis2.772602838
Mean64.33512967
Median Absolute Deviation (MAD)26
Skewness1.796798017
Sum191011
Variance6046.704699
MonotonicityNot monotonic
2021-12-25T18:20:56.779030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723475918
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:57.091662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.856408695
Coefficient of variation (CV)1.547382888
Kurtosis190.8435373
Mean5.723475918
Median Absolute Deviation (MAD)2
Skewness10.76715289
Sum16993
Variance78.43597498
MonotonicityNot monotonic
2021-12-25T18:20:57.407185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2784
26.4%
3500
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2784
26.4%
3500
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1670
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1581.736612
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:57.683545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median639
Q31399
95-th percentile4403
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5704.365442
Coefficient of variation (CV)3.60639401
Kurtosis516.908628
Mean1581.736612
Median Absolute Deviation (MAD)420
Skewness18.74065094
Sum4696176
Variance32539785.09
MonotonicityNot monotonic
2021-12-25T18:20:58.027497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2468
 
0.3%
2888
 
0.3%
2728
 
0.3%
848
 
0.3%
2608
 
0.3%
1147
 
0.2%
5167
 
0.2%
Other values (1660)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7423375
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:58.354355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8901568
Coefficient of variation (CV)2.198835074
Kurtosis354.8900356
Mean122.7423375
Median Absolute Deviation (MAD)44
Skewness15.70854849
Sum364422
Variance72840.69673
MonotonicityNot monotonic
2021-12-25T18:20:58.678915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2629
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.98999382
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:58.978740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.95658654
Q324.97962963
95-th percentile89.991
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.8602963

Descriptive statistics

Standard deviation119.5121529
Coefficient of variation (CV)3.62267885
Kurtosis813.2366272
Mean32.98999382
Median Absolute Deviation (MAD)5.976425248
Skewness25.16116391
Sum97947.29165
Variance14283.15468
MonotonicityNot monotonic
2021-12-25T18:20:59.247776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2956)2956
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.28911989
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:20:59.552930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92307692
median48.25
Q385.33333333
95-th percentile200.6
Maximum366
Range365
Interquartile range (IQR)59.41025641

Descriptive statistics

Standard deviation63.49861816
Coefficient of variation (CV)0.9436684305
Kurtosis4.910479697
Mean67.28911989
Median Absolute Deviation (MAD)26.25
Skewness2.06658447
Sum199781.397
Variance4032.074508
MonotonicityNot monotonic
2021-12-25T18:20:59.858993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
4617
 
0.6%
2117
 
0.6%
1117
 
0.6%
116
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138056482
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:21:00.472485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008894164194
Q10.01633986928
median0.02590673575
Q30.04945054945
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03311068017

Descriptive statistics

Standard deviation0.4081543769
Coefficient of variation (CV)3.58641582
Kurtosis989.3812771
Mean0.1138056482
Median Absolute Deviation (MAD)0.01218850234
Skewness24.88077608
Sum337.8889694
Variance0.1665899954
MonotonicityNot monotonic
2021-12-25T18:21:00.747211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0344827586215
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2635
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8773998
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:21:01.091117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.8177717
Coefficient of variation (CV)8.10891217
Kurtosis596.4015287
Mean34.8773998
Median Absolute Deviation (MAD)1
Skewness21.97906809
Sum103551
Variance79985.89197
MonotonicityNot monotonic
2021-12-25T18:21:01.408670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.7%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.2283257
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:21:01.704608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.25
Q3281.5
95-th percentile599.52
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation283.8485216
Coefficient of variation (CV)1.201585461
Kurtosis102.8152374
Mean236.2283257
Median Absolute Deviation (MAD)83
Skewness7.703170978
Sum701361.899
Variance80569.98322
MonotonicityNot monotonic
2021-12-25T18:21:02.016798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
869
 
0.3%
739
 
0.3%
888
 
0.3%
758
 
0.3%
1368
 
0.3%
608
 
0.3%
2887
 
0.2%
Other values (1969)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48958744
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-25T18:21:02.321315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.14285714
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47619048

Descriptive statistics

Standard deviation15.45753673
Coefficient of variation (CV)0.8838136851
Kurtosis29.33578443
Mean17.48958744
Median Absolute Deviation (MAD)6.6
Skewness3.437111879
Sum51926.58512
Variance238.9354417
MonotonicityNot monotonic
2021-12-25T18:21:02.635655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
839
 
1.3%
1639
 
1.3%
1738
 
1.3%
1438
 
1.3%
1136
 
1.2%
536
 
1.2%
736
 
1.2%
1535
 
1.2%
Other values (896)2589
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2021-12-25T18:20:50.436438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:11.811935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:15.246456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:18.453538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:21.647787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:24.768093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:28.031575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:31.262038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:34.464732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:37.607169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:40.788998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:43.976392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:47.030061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:50.673761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:12.070873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:15.493462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:18.695539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:21.891319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:24.979686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:28.260679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:31.502886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:34.692516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:37.861786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:41.035173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:44.215643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:47.267599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:50.914776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:12.303920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:15.718259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:18.919578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:22.126751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:25.181768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:28.487057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:31.791856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:34.904477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:38.088710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:41.282623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:44.441630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:47.505667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:51.157980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:12.873658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:15.951062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:19.303671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:22.350511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:25.416527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:28.736396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:32.014786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:35.122300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:38.330387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:41.536890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:44.674642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:48.002327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:51.401325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:13.121404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:16.197575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:19.569778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:22.623059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:25.649643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:28.989943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:32.269561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:35.330849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:38.566505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:41.799438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:44.918002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:48.252077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:51.641946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:13.366129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:16.409565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:19.775027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:22.837723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:25.872100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:29.225590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:32.505731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:35.535657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:38.818060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:42.057191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:45.132140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:48.487890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:51.897313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:13.612265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:16.667683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:20.007033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:23.086062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:26.141867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:29.488003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:32.780393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:35.768408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:39.090147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:42.309860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:45.384591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:48.739660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:52.135964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:13.831150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:16.934568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:20.260755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:23.324672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:26.388629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:29.755213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:33.037415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:36.002996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:39.338135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:42.566234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:45.633038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:48.998191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:52.384843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:14.059136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:17.203116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:20.466547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:23.549161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:26.622062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:29.993890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:33.258433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:36.448544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:39.569977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:42.776565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:45.840068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:49.222528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:52.622017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:14.303392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:17.473889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:20.706869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:23.788494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:27.031361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:30.263955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:33.509192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:36.672171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:39.834456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:43.026348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:46.081717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:49.467762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:52.860038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:14.552827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:17.729189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:20.952939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:24.042235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:27.284020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:30.525634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:33.757060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:36.919976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:40.081685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:43.268871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:46.306509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:49.720520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:53.128037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:14.778506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:17.964777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:21.174759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:24.277510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:27.541585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:30.770161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:34.005924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:37.147248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:40.302502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:43.506885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:46.537330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:49.953358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:53.347128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:15.019219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:18.208931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:21.413581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:24.539225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:27.777027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:31.015896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:34.233470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:37.371377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:40.541981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:43.737581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:46.785551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-25T18:20:50.198538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2021-12-25T18:21:02.888336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-25T18:21:03.215517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-25T18:21:03.539973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-25T18:21:03.874544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-25T18:20:53.742402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-25T18:20:54.299003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
2960563717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
2961563817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
2962564913596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
29635655148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
2964565912479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
2965568014126706.137.03.0508.015.047.0753333.00.75000050.0169.3333334.666667
29665686135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
2967569615060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
2968571512558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000